The model is developed from an assembly of 14 datasets with 3, 410 CT scans and evaluated on 6, 162 external CT scans from 3 datasets.
The proposed pipeline is effective and robust in extracting muscle groups on 2D single slice CT thigh images. The container is available for public use at https://github. com/MASILab/DA_CT_muscle_seg
We evaluate our proposed approach on multi-organ segmentation with both non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge contrast-enhance CT (CECT) datasets.
Hierarchical transformers (e. g., Swin Transformers) reintroduced several ConvNet priors and further enhanced the practical viability of adapting volumetric segmentation in 3D medical datasets.
1 code implementation • 28 Sep 2022 • Xin Yu, Qi Yang, Yinchi Zhou, Leon Y. Cai, Riqiang Gao, Ho Hin Lee, Thomas Li, Shunxing Bao, Zhoubing Xu, Thomas A. Lasko, Richard G. Abramson, Zizhao Zhang, Yuankai Huo, Bennett A. Landman, Yucheng Tang
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis.
External experiments on 20 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset that contains longitudinal single abdominal slices validate that our method can harmonize the slice positional variance in terms of muscle and visceral fat area.
Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitatively characterizing these relationships.
In cross-validation on screening chest CTs from the NLST, our methods (0. 785 and 0. 786 AUC respectively) significantly outperform a cross-sectional approach (0. 734 AUC) and match the discriminative performance of the leading longitudinal medical imaging algorithm (0. 779 AUC) on benign versus malignant classification.
1 code implementation • 30 Aug 2022 • Tianyuan Yao, Chang Qu, Jun Long, Quan Liu, Ruining Deng, Yuanhan Tian, Jiachen Xu, Aadarsh Jha, Zuhayr Asad, Shunxing Bao, Mengyang Zhao, Agnes B. Fogo, Bennett A. Landman, Haichun Yang, Catie Chang, Yuankai Huo
In order to extract and separate compound figures into usable individual images for downstream learning, we propose a simple compound figure separation (SimCFS) framework without using the traditionally required detection bounding box annotations, with a new loss function and a hard case simulation.
1 code implementation • 15 Aug 2022 • Ruining Deng, Can Cui, Lucas W. Remedios, Shunxing Bao, R. Michael Womick, Sophie Chiron, Jia Li, Joseph T. Roland, Ken S. Lau, Qi Liu, Keith T. Wilson, Yaohong Wang, Lori A. Coburn, Bennett A. Landman, Yuankai Huo
Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations.
We evaluate the validity of the proposed method in automatic BC assessment using lung screening CT with limited FOV.
no code implementations • 3 Jun 2022 • Lena Maier-Hein, Annika Reinke, Patrick Godau, Minu D. Tizabi, Evangelia Christodoulou, Ben Glocker, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael A. Riegler, Manuel Wiesenfarth, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Beth Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Florian Kofler, Annette Kopp-Schneider, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Nasir Rajpoot, Nicola Rieke, Julio Saez-Rodriguez, Clarisa Sánchez Gutiérrez, Shravya Shetty, Maarten van Smeden, Carole H. Sudre, Ronald M. Summers, Abdel A. Taha, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Paul F. Jäger
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem.
Transformer, the latest technological advance of deep learning, has gained prevalence in natural language processing or computer vision.
In this paper, we propose a novel unsupervised approach that leverages pairwise contrast-enhanced CT (CECT) context to compute non-contrast segmentation without ground-truth label.
The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice.
Thus, there are still open questions on how to effectively predict brain cancer survival from the incomplete radiological, pathological, genomic, and demographic data (e. g., one or more modalities might not be collected for a patient).
no code implementations • 4 Mar 2022 • Xin Yu, Yucheng Tang, Yinchi Zhou, Riqiang Gao, Qi Yang, Ho Hin Lee, Thomas Li, Shunxing Bao, Yuankai Huo, Zhoubing Xu, Thomas A. Lasko, Richard G. Abramson, Bennett A. Landman
Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology.
Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant.
Ranked #1 on Medical Object Detection on MoNuSeg 2018
A Bad-GAN generates pseudo anomalies at the low-density area of inlier distribution, and thus the inlier/outlier distinction can be approximated.
Our contribution is three-fold: (1) a single deep network framework is proposed to tackle missing stain in MxIF; (2) the proposed 'N-to-N' strategy reduces theoretical four years of computational time to 20 hours when covering all possible missing stains scenarios, with up to five missing stains (e. g., '(N-1)-to-1', '(N-2)-to-2'); and (3) this work is the first comprehensive experimental study of investigating cross-stain synthesis in MxIF.
Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning.
To our knowledge, it is the first generative adversarial model that addresses multi-modal missing imputation by modeling the joint distribution of image and non-image data.
1 code implementation • 19 Jul 2021 • Tianyuan Yao, Chang Qu, Quan Liu, Ruining Deng, Yuanhan Tian, Jiachen Xu, Aadarsh Jha, Shunxing Bao, Mengyang Zhao, Agnes B. Fogo, Bennett A. Landman, Catie Chang, Haichun Yang, Yuankai Huo
Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations.
1 code implementation • 10 Jun 2021 • Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, AnnetteKopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M. Summers, Bram van Ginneken, Michel Bilello, Patrick Bilic, Patrick F. Christ, Richard K. G. Do, Marc J. Gollub, Stephan H. Heckers, William R. Jarnagin, Maureen K. McHugo, Sandy Napel, Jennifer S. Goli Pernicka, Kawal Rhode, Catalina Tobon-Gomez, Eugene Vorontsov, Henkjan Huisman, James A. Meakin, Sebastien Ourselin, Manuel Wiesenfarth, Pablo Arbelaez, Byeonguk Bae, Sihong Chen, Laura Daza, Jianjiang Feng, Baochun He, Fabian Isensee, Yuanfeng Ji, Fucang Jia, Namkug Kim, Ildoo Kim, Dorit Merhof, Akshay Pai, Beomhee Park, Mathias Perslev, Ramin Rezaiifar, Oliver Rippel, Ignacio Sarasua, Wei Shen, Jaemin Son, Christian Wachinger, Liansheng Wang, Yan Wang, Yingda Xia, Daguang Xu, Zhanwei Xu, Yefeng Zheng, Amber L. Simpson, Lena Maier-Hein, M. Jorge Cardoso
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Medical image segmentation, or computing voxelwise semantic masks, is a fundamental yet challenging task to compute a voxel-level semantic mask.
1 code implementation • 12 Apr 2021 • Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Keyvan Farahani, Bram van Ginneken, Ben Glocker, Patrick Godau, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Alexandros Karargyris, Alan Karthikesalingam, Bernhard Kainz, Emre Kavur, Hannes Kenngott, Jens Kleesiek, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, M. Alican Noyan, Jens Petersen, Gorkem Polat, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clarisa Sanchez Gutierrez, Julien Schroeter, Anindo Saha, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Annette Kopp-Schneider, Paul Jäger, Lena Maier-Hein
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
The primary advantage being that active learning frameworks select data points that can accelerate the learning process of a model and can reduce the amount of data needed to achieve full accuracy as compared to a model trained on a randomly acquired data set.
We combine the anatomical prior with corresponding extracted patches to preserve the anatomical locations and boundary information for performing high-resolution segmentation across all organs in a single model.
no code implementations • 23 Dec 2020 • Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes B. Fogo, Raymond Harris, Mark P. de Caestecker, Mattias Heinrich, Jeffrey M. Spraggins, Yuankai Huo, Bennett A. Landman
However, there is no abdominal and retroperitoneal organs atlas framework for multi-contrast CT.
no code implementations • 5 Dec 2020 • Kaiwen Xu, Riqiang Gao, Mirza S. Khan, Shunxing Bao, Yucheng Tang, Steve A. Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Mattias P. Heinrich, Bennett A. Landman
For the entire study cohort, the optimized pipeline achieves a registration success rate of 91. 7%.
no code implementations • 18 Nov 2020 • Cailey I. Kerley, Leon Y. Cai, Chang Yu, Logan M. Crawford, Jason M. Elenberger, Eden S. Singh, Kurt G. Schilling, Katherine S. Aboud, Bennett A. Landman, Tonia S. Rex
We then applied independent component analysis (ICA) both to each PCA space individually and together in a joint ICA approach.
The network can be trained end-to-end with both medical image features and CDEs/biomarkers, or make a prediction with single modality.
With both embedding simulation and empirical validation via the four cohorts from the ISBI cell tracking challenge, the proposed Faster Mean-shift algorithm achieved 7-10 times speedup compared to the state-of-the-art embedding based cell instance segmentation and tracking algorithm.
To this end, we propose a fully-automated and multi-stage liver tumor characterization framework designed for dynamic contrast computed tomography (CT).
In this work, we propose CircleNet, a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus.
But in some learning problems, partial label information can be inferred from otherwise unlabeled examples and used to further improve the model.
Herein, we explore the possibility of using deep learning on single shell data (using the b=1000 s/mm2 from the Human Connectome Project (HCP)) to estimate the information content captured by 8th order MT-CSD using the full three shell data (b=1000, 2000, and 3000 s/mm2 from HCP).
no code implementations • 10 Feb 2020 • Yuchen Xu, Olivia Tang, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
We built on a pre-trained 3D U-Net model for abdominal multi-organ segmentation and augmented the dataset either with outlier data (e. g., exemplars for which the baseline algorithm failed) or inliers (e. g., exemplars for which the baseline algorithm worked).
no code implementations • 10 Feb 2020 • Yuchen Xu, Olivia Tang, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
A 2015 MICCAI challenge spurred substantial innovation in multi-organ abdominal CT segmentation with both traditional and deep learning methods.
To improve performance on the primary task, we propose an Internal-Transfer Weighting (ITW) strategy to suppress the loss functions on auxiliary tasks for the final stages of training.
Veterans with mild traumatic brain injury (mTBI) have reported auditory and visual dysfunction that persists beyond the acute incident.
no code implementations • 1 Dec 2019 • Yuankai Huo, Yucheng Tang, Yunqiang Chen, Dashan Gao, Shizhong Han, Shunxing Bao, Smita De, James G. Terry, Jeffrey J. Carr, Richard G. Abramson, Bennett A. Landman
We evaluate the effectiveness of both with and without using soft tissue window normalization on multisite CT cohorts.
no code implementations • 14 Nov 2019 • Yucheng Tang, Ho Hin Lee, Yuchen Xu, Olivia Tang, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Camilo Bermudez, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy.
Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances.
no code implementations • 13 Nov 2019 • Vishwesh Nath, Kurt G. Schilling, Colin B. Hansen, Prasanna Parvathaneni, Allison E. Hainline, Camilo Bermudez, Andrew J. Plassard, Vaibhav Janve, Yurui Gao, Justin A. Blaber, Iwona Stępniewska, Adam W. Anderson, Bennett A. Landman
Confocal histology provides an opportunity to establish intra-voxel fiber orientation distributions that can be used to quantitatively assess the biological relevance of diffusion weighted MRI models, e. g., constrained spherical deconvolution (CSD).
no code implementations • 12 Nov 2019 • Ho Hin Lee, Yucheng Tang, Olivia Tang, Yuchen Xu, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
The contributions of the proposed method are threefold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional true/false, and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method.
Annual low dose computed tomography (CT) lung screening is currently advised for individuals at high risk of lung cancer (e. g., heavy smokers between 55 and 80 years old).
Biomedical challenges have become the de facto standard for benchmarking biomedical image analysis algorithms.
no code implementations • 11 Sep 2019 • Riqiang Gao, Yuankai Huo, Shunxing Bao, Yucheng Tang, Sanja L. Antic, Emily S. Epstein, Aneri B. Balar, Steve Deppen, Alexis B. Paulson, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman
To model both regular and irregular longitudinal samples, we generalize the LSTM model with the Distanced LSTM (DLSTM) for temporally varied acquisitions.
Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI).
no code implementations • 15 Jul 2019 • Vishwesh Nath, Ilwoo Lyu, Kurt G. Schilling, Prasanna Parvathaneni, Colin B. Hansen, Yucheng Tang, Yuankai Huo, Vaibhav A. Janve, Yurui Gao, Iwona Stepniewska, Adam W. Anderson, Bennett A. Landman
In the in-vivo human data, Deep SHORE was more consistent across scanners with 0. 63 relative to other multi-shell methods 0. 39, 0. 52 and 0. 57 in terms of ACC.
Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD).
2 code implementations • 28 Mar 2019 • Yuankai Huo, Zhoubing Xu, Yunxi Xiong, Katherine Aboud, Prasanna Parvathaneni, Shunxing Bao, Camilo Bermudez, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman
To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data.
9 code implementations • 25 Feb 2019 • Amber L. Simpson, Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram van Ginneken, Annette Kopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M. Summers, Patrick Bilic, Patrick F. Christ, Richard K. G. Do, Marc Gollub, Jennifer Golia-Pernicka, Stephan H. Heckers, William R. Jarnagin, Maureen K. McHugo, Sandy Napel, Eugene Vorontsov, Lena Maier-Hein, M. Jorge Cardoso
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization.
The results show that the AUC obtained from clinical demographics alone was 0. 635 while the attention network alone reached an accuracy of 0. 687.
Recently, we obtained a clinically acquired, multi-sequence MRI brain cohort with 1480 clinically acquired, de-identified brain MRI scans on 395 patients using seven different MRI protocols.
For instance, in a typical sample of clinical TBI imaging cohort, only ~15% of CT scans actually contain whole brain CT images suitable for volumetric brain analyses; the remaining are partial brain or non-brain images.
no code implementations • 26 Nov 2018 • Camilo Bermudez, William Rodriguez, Yuankai Huo, Allison E. Hainline, Rui Li, Robert Shults, Pierre D. DHaese, Peter E. Konrad, Benoit M. Dawant, Bennett A. Landman
We show an improvement in the classification of intraoperative stimulation coordinates as a positive response in reduction of symptoms with AUC of 0. 670 compared to a baseline registration-based approach, which achieves an AUC of 0. 627 (p < 0. 01).
From the results, the proposed AID-Net achieved the superior performance on classification accuracy (0. 9272) and AUC (0. 9627).
no code implementations • 9 Nov 2018 • Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Hyeonsoo Moon, Prasanna Parvathaneni, Tamara K. Moyo, Michael R. Savona, Albert Assad, Richard G. Abramson, Bennett A. Landman
A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network.
SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality.
no code implementations • 9 Oct 2018 • Vishwesh Nath, Prasanna Parvathaneni, Colin B. Hansen, Allison E. Hainline, Camilo Bermudez, Samuel Remedios, Justin A. Blaber, Kurt G. Schilling, Ilwoo Lyu, Vaibhav Janve, Yurui Gao, Iwona Stepniewska, Baxter P. Rogers, Allen T. Newton, L. Taylor Davis, Jeff Luci, Adam W. Anderson, Bennett A. Landman
Herein, we propose a data-driven tech-nique using a neural network design which exploits two categories of data.
By indexing the dictionary, the whole brain probabilistic atlases adapt to each new subject quickly and can be used as spatial priors for visualization and processing.
no code implementations • 6 Jun 2018 • Lena Maier-Hein, Matthias Eisenmann, Annika Reinke, Sinan Onogur, Marko Stankovic, Patrick Scholz, Tal Arbel, Hrvoje Bogunovic, Andrew P. Bradley, Aaron Carass, Carolin Feldmann, Alejandro F. Frangi, Peter M. Full, Bram van Ginneken, Allan Hanbury, Katrin Honauer, Michal Kozubek, Bennett A. Landman, Keno März, Oskar Maier, Klaus Maier-Hein, Bjoern H. Menze, Henning Müller, Peter F. Neher, Wiro Niessen, Nasir Rajpoot, Gregory C. Sharp, Korsuk Sirinukunwattana, Stefanie Speidel, Christian Stock, Danail Stoyanov, Abdel Aziz Taha, Fons van der Sommen, Ching-Wei Wang, Marc-André Weber, Guoyan Zheng, Pierre Jannin, Annette Kopp-Schneider
International challenges have become the standard for validation of biomedical image analysis methods.
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy.
Real and synthesized images were then assessed in a blinded manner by two imaging experts providing an image quality score of 1-5.
Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT.
However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods.
Herein, the MaCRUISE surface parcellation (MaCRUISEsp) method is proposed to perform the surface parcellation upon the inner, central and outer surfaces that are reconstructed from MaCRUISE.
(2) The proposed algorithm is a longitudinal generalization of a lead-ing joint label fusion method (JLF) that has proven adaptable to a wide variety of applications.